The Experts below are selected from a list of 97008 Experts worldwide ranked by ideXlab platform
Ananthram Swami - One of the best experts on this subject based on the ideXlab platform.
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WSDM - SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019Co-Authors: Chuxu Zhang, Ananthram Swami, Nitesh V ChawlaAbstract:Representation learning in Heterogeneous Networks faces challenges due to Heterogeneous structural information of multiple types of nodes and relations, and also due to the unstructured attribute or content (e.g., text) associated with some types of nodes. While many recent works have studied homogeneous, Heterogeneous, and attributed Networks embedding, there are few works that have collectively solved these challenges in Heterogeneous Networks. In this paper, we address them by developing a Semantic-aware Heterogeneous Network Embedding model (SHNE). SHNE performs joint optimization of Heterogeneous SkipGram and deep semantic encoding for capturing both Heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Extensive experiments demonstrate that SHNE outperforms state-of-the-art baselines in various Heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation, relevance search, and class visualization.
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metapath2vec scalable representation learning for Heterogeneous Networks
Knowledge Discovery and Data Mining, 2017Co-Authors: Yuxiao Dong, Nitesh V Chawla, Ananthram SwamiAbstract:We study the problem of representation learning in Heterogeneous Networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the Heterogeneous neighborhood of a node and then leverages a Heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in Heterogeneous Networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various Heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
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KDD - metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017Co-Authors: Yuxiao Dong, Nitesh V Chawla, Ananthram SwamiAbstract:We study the problem of representation learning in Heterogeneous Networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the Heterogeneous neighborhood of a node and then leverages a Heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in Heterogeneous Networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various Heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
Xiaoniu Yang - One of the best experts on this subject based on the ideXlab platform.
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VTC Spring - Delay Performance Optimization of Multiaccess for Uplink in Heterogeneous Networks
2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014Co-Authors: Jie Zheng, Qin Liu, Xiaoniu YangAbstract:Heterogeneous Networks are an attractive means of improving the network capacity. Heterogeneous network are typically composed of multiple radio access points (macro, pico and femto) where the base stations are transmitting with variable power. In this study, we consider the uplink of Heterogeneous Networks, where each terminal can simultaneously connected to multiple radio access points and requests delay-sensitive traffic (e.g., real-time video). We adopt the framework of Hou, Borkar,and Kumar, and study the delay in Heterogeneous Networks and analyze the multiaccess transmission delay by considering three extreme scheduling schemes. Moreover, a parallel-aggregation multiple radio access points (PA-MRA) algorithm for packet scheduling is presented to improve the transmission delay in Heterogeneous Networks. Analysis and numerical results show that the proposed algorithm obtains the optimal and robust transmission delay gain compared with the three extreme scheduling policies.
Torsten Dudda - One of the best experts on this subject based on the ideXlab platform.
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VTC Spring - On Heterogeneous Networks Mobility Robustness
2013 IEEE 77th Vehicular Technology Conference (VTC Spring), 2013Co-Authors: Kristina Zetterberg, Pradeepa Ramachandra, Fredrik Gunnarsson, Mehdi Amirijoo, Stefan Wager, Torsten DuddaAbstract:Heterogeneous network 3GPP LTE deployments are considered as a prime candidate to meet increasing demand for mobile broadband service coverage and capacity. Such deployments also need to support mobility that is as robust as in traditional macro deployments. In this paper, we analyze the handover performance in Heterogeneous deployments to better understand potential behavior of automatic handover parameter adjustments. Such a Self-Organizing Network (SON) feature is commonly known as Mobility Robustness Optimization (MRO). The analyses indicate difficult challenges with handovers from low power to high power base stations, and an evolved handover procedure that improves the user experience during such times is presented. These results are key for developing an MRO algorithm for Heterogeneous Networks.
Abdelhamid Mellouk - One of the best experts on this subject based on the ideXlab platform.
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Quality of service models for Heterogeneous Networks: overview and challenges
Annals of Telecommunications - Annales Des Télécommunications, 2008Co-Authors: Hesham El-sayed, Laurent George, Abdelhamid Mellouk, Sherali ZeadallyAbstract:The proliferation and convergence of different types of wired, wireless and mobile Networks (such as WiMAX, Wireless Mesh Networks, WPANs, WLANs, etc) and cellular-based Networks are crucial for the success of next-generation Networks. Traditional wired/wireless Networks can hardly meet the requirements of future integrated-service Networks which are expected to carry multimedia traffic with various quality of service (QoS) requirements. Therefore, it is necessary to develop efficient global control mechanisms that can maintain QoS requirements to maximize network resources utilization, and minimize operational costs on all the types of wireless mobile Networks. In this paper, we present an overview of QoS paradigms for Heterogeneous Networks and focus on those based on deterministic and probabilistic QoS.
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Quality of service based routing algorithms for Heterogeneous Networks [Guest editorial]
IEEE Communications Magazine, 2007Co-Authors: Abdelhamid Mellouk, Pascal Lorenz, Azzedine Boukerche, Moon Ho LeeAbstract:Discusses the need to develop a high quality control mechanism to check network traffic load for Heterogeneous Networks and ensure QoS requirements. The five articles in this special section are devoted to quality of service based routing algorithms for Heterogeneous Networks and are briefly summarized.
Yuxiao Dong - One of the best experts on this subject based on the ideXlab platform.
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metapath2vec scalable representation learning for Heterogeneous Networks
Knowledge Discovery and Data Mining, 2017Co-Authors: Yuxiao Dong, Nitesh V Chawla, Ananthram SwamiAbstract:We study the problem of representation learning in Heterogeneous Networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the Heterogeneous neighborhood of a node and then leverages a Heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in Heterogeneous Networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various Heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
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KDD - metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017Co-Authors: Yuxiao Dong, Nitesh V Chawla, Ananthram SwamiAbstract:We study the problem of representation learning in Heterogeneous Networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the Heterogeneous neighborhood of a node and then leverages a Heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in Heterogeneous Networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various Heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
